Create Alert Alert. Share This Paper. Background Citations. Methods Citations. Results Citations. Figures and Tables from this paper. Citation Type. Has PDF. Publication Type. More Filters. This paper considers a new class of robust estimators in a linear instrumental variables IV model with many instruments.
The estimators are generalized method of moments GMM estimators, and the … Expand. View 2 excerpts, cites background. Simple and trustworthy cluster-robust GMM inference. This paper develops a new asymptotic theory for two-step GMM estimation and inference in the presence of clustered dependence.
The key feature of alternative asymptotics is the number of clusters G … Expand. This paper develops asymptotic distribution theory for generalized method of moments GMM estimators and test statistics when some of the parameters are well identified, but others are poorly … Expand. Estimation and Inference with Weak Identi…cation. Properties of GMM estimators are sensitive to the choice of instruments. In an effort to improve the small sample properties of generalized method of moments GMM estimators, a number of alternative estimators have been suggested.
These include empirical likelihood EL , … Expand. Highly Influenced. View 3 excerpts, cites background and methods. A number of recent studies have concluded that the most commonly used efficient two-step generalized method of moments GMM estimator may have large bias in applications.
This problem has motivated … Expand. View 5 excerpts, cites background and methods. Haile, P. Dinardo, J. Fortin, and T. Subramanian, S.
Lewbel, A. Oreopoulos, P. Abadie, A. Ahn, H. Vella, F. Don't show me this again. This is one of over 2, courses on OCW. Explore materials for this course in the pages linked along the left.
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Use OCW to guide your own life-long learning, or to teach others. Newey shows that how the nonparametric component is estimated, using series methods, or using kernel regression, does not matter for the asymptotic distribution of the estimator for the parametric component.
This insight, and the methods for deriving the variance in general, inspired generations of researchers to investigate semiparametric models. It is at the basis of the work on estimating average treatment effects without distributional and functional form assumptions that has been very influential in empirical work in the last two decades. Whitney has served as a mentor for generations of econometrics students at MIT, and has been a leader in promoting research in econometrics that is relevant for empirical research, both by example, and by promoting such work as co-editor at Econometrica.
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